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Financing Constraints and Inventory Investment: A Comparative Study with High-FrequencyPanel DataAuthor(s): Robert E. Carpenter, Steven M. Fazzari, Bruce C. PetersenReviewed work(s):Source: The Review of Economics and Statistics, Vol. 80, No. 4 (Nov., 1998), pp. 513-519Published by: The MIT PressStable URL: http://www.jstor.org/stable/2646834 .

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FINANCING CONSTRAINTS AND INVENTORY INVESTMENT:A COMPARATIVE STUDY WITH HIGH-FREQUENCY PANEL DATARobertE. Carpenter, teven M. Fazzari,and BruceC. Petersen*

Abstract-This study provides new evidence of the importanceoffinancingconstraints or explaining the dramaticcycles in inventoryinvestment.We compare heempiricalperformance f different inancialvariables coverageratio, cash stocks, and cash flow) used in previousresearcho test or the presence f financing onstraints. hecomparisonsundertakenn a common frameworkwith an identical sample andhigh-frequency quarterly) irmpanel data. Cash flow is much moresuccessful than cash stocks or coveragein explaining he facts aboutinventory nvestmentacross firm size, different nventolycycles, anddifferentmanufacturingectors.

I. IntroductionIN HELASTdecade herehas been a dramatic evivalofresearch on financing constraints and firm behavior. Thenew literature covers a broad range of issues, includinginventory investment, R&D, physical investment, pricingunder impeifect information, business formation and sur-vival, tax policy, the business cycle, and the transmission ofmonetary policy.' This paper makes two principal contribu-tions to this literature.First, we use high-frequency (quar-terly) firm panel data to provide new evidence supportingthe importanceof financingconstraints for inventory invest-ment. Second, we provide the first comparative study ofdifferent financial variables used to test for the presence offinancingconstraints.Contributing to the explanation of inventory cycles is achallenge for the financing constraint literature. Inventoryinvestment fluctuations account for a surprisingly largefraction of the aggregate cycle. For example, Blinder andMaccini (1991) reportthat declines in inventory investmentaveraged 87% of the drop in aggregate U.S. output duringpostwar recessions. Other researchers in the inventoryliterature(e.g., Lovell (1994, p. 34)) point to the potentialinfluence of financing constraints on inventory investmentas a major unanswered question.In addition to its aggregate volatility, there are importantsectoral differences in the cyclical pattern of inventoryinvestment. Stanback (1962, p. 23) and Zarnowitz (1985, p.527) identify much larger cyclical movements in durableinventory investment compared with nondurables.Figure 1extends Stanback's evidence to the present, showing quar-

terly growth rates of real inventory stocks for durable andnondurable manufacturing.2The durable series clearly dis-plays greater cyclical volatility. We explore the ability of thefinancial variables to help explain this sectoral heterogene-ity, a new test of the ability of financing constraints toexplain inventory behavior.The studies that motivate our paper are Gertler andGilchrist (1994), hereafter GG, Kashyap et al. (1994),hereafter KLS, and Carpenter et al. (1994), hereafterCFP3Each study employs different financial variables and econo-metric approaches and each emphasizes a different channelthrough which financing constraints operate. GG employ thecoverage ratio, a measure of firms' ability to meet interestpayments. Bemanke and Gertler (1995, p. 38) state that thecoverage ratio is a good measure of the strength of a firm'sbalance sheet and cite the GG findings as importantevidencesupporting the presence of a "balance sheet channel" in thetransmission mechanism for monetary policy. KLS includethe stock of cash in an inventory regression. Their study isthe first to provide micro evidence in support of a "banklending channel" in the transmission mechanism.4 In CFPwe focus on the impact of cash flow for inventory invest-ment. We emphasize thatlarge fluctuations in cash flow overthe business cycle may cause firms to make large adjust-ments to inventories (a liquid, readily reversible investmentwith low adjustment costs) to partiallyoffset shocks to cashflow, the primarysource of finance for most firms.We compare the performance of these three financialvariables in a common econometric framework with anidentical sample of quarterly firm panel data. This kind ofdata provides several advantages. Quarterlydata are desir-able to study a high-frequency phenomenon like inventoryinvestment, yet few studies of financing constraints haveutilized it.5Oursample improves upon the data used by GG(aggregate times series, disaggregated into two firm-sizeclasses) and KLS (individual annual cross sections). Withpanel data we control for individual firm effects that arelikely to bias results from cross-sectional regressions. Inaddition, the large number of firms combined with thehigh-frequencydata allows us to examine brief periods (twoto four years), permitting multiple comparisons of theperformanceof the financial variables over different cyclicaleceived for publicationSeptember3, 1996. Revision accepted forpublicationNovember 6, 1997.* Emory University; WashingtonUniversity and the Jerome LevyEconomics nstitute; ndWashington niversity, espectively.We thank he Levy Institute or financial upportand Ben Herzon orexcellentresearch ssistance,andwe acknowledge hehelpfulcommentsof Lee Benham,RobertChirinko,MarkGertler, imon Gilchrist,CharlesHimmelberg,Glenn Hubbard,John Keating, Michael Lovell, LouisMaccini, Alistair Milne, Donald Morgan, Dorothy Petersen, StevenSharpe,andVictorZarnowitz.We also receivedhelpfulcomments romseminar articipantstDalhousieUniversity nd heNationalAutonomousUniversityof Mexico and the 1996 ISIR-ASSA,MidwesternEconomics,EasternFinance,and WesternFinanceAssociationmeetings.In additionwe thank heeditorand woanonymouseferees orvaluable omments.ISeeHubbard1998)for areviewof the iterature.

2 Both series n the figurearethree-quarter ovingaverages o make hecyclicalpatternsmoreobvious.3 Other empiricalresearch on inventory, nvestment,and financingconstraintsncludesCalomiris t al. (1995) andMilne(1995).4 This studyhas been nfluentialn establishing linkbetweenmonetarypolicy and nventorynvestmenthatdoes notrelyon an nterest ateeffect.See the discussion n theNBER Reporter (Fall 1995).5 The evidence n Carpenter nd Levy (1998) supports his point.Theyestimate hatoverthree-quartersf the variationn monthly, ndustry-levelinventory nvestment s contained n the high-frequency andsof thespectrum ndcannotbe detectedwith annualdata.

? 1998 by the Presidentand Fellows of HarvardCollege and the Massachusetts nstituteof Technology [ 513 ]


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514 THE REVIEW OF ECONOMICSAND STATISTICS

episodes, as well as across firm-size classes and manufactur-ing sectors.We find that the results for cash stocks and coverage areweaker than reported in previous research. The cash stockvariable emphasized by KLS is statistically significant inonly two of our twelve regressions, and only one of thesignificant effects is for small firms, the group most likely tobe financially constrained. The coverage variable studied byGG has significanteffects in many of ourregressions, but thecoverage coefficients for small firms do not exceed those forlarge firms in a majority of comparisons. In contrast, thestrong empirical performanceof cash flow is consistent withthe presence of financing constraints.The cash flow coeffi-cients for small firms always exceed those for large firms.Cash flow also helps to explain different features of the threeinventory cycles we study andthe greater amplitudeof thesecycles in the durablesector. These results have implicationsfor policy, including the monetary policy transmissionmechanism, which we discuss in the conclusion.

II. Empirical Specification and Data DescriptionA. EstimatingEquation

We employ a widely used inventory investment model(see Blinder and Maccini (1991)) augmented by financialvariables. For firmj at time t (measuredin quarters) etANjt = X(Njt Njp) at(S -Et-,SjSt) (1)

where ANj,tis inventory investment in period t, Nj,tand N*jtdenote the actual and target stocks of inventories at thebeginning of period t, and Sj,t and Et- Sj,trepresent actualand forecasted levels of sales. The stock adjustment termrelates inventoryinvestment to the gap between targetstocksand actual stocks, with speed of adjustmentX. Following theliterature,we specify target inventories as a linear function

of expected sales and include a firm effect that controls forunobservable differences across firms. The second term inequation (1) arises from inventory's role as a buffer stock.The parametercxmeasures inventory's response to unantici-pated sales shocks. As in Blinder (1986), expected salesfollow an autoregressive process, again including a firmeffect.These assumptions and the substitution of the targetinventory stock and sales forecast into equation (1) yield(see CFP)

ANj,t = -Nj,t -Sjt + 8Sjt-I + 82Sj,t-2 + (i (2)+ 0i,t + uj,t

where Oj s a firm fixed effect, 0i,t is a quarterdummyvariable for industry i, and ujpts a stochastic errorterm.Thefirm fixed effect O captures time-invariant differences in thedeterminants of inventory investment, such as the rate ofobsolescence and storage costs. Because these factors arelikely correlated with financial factors, failure to control forthem will lead to inconsistent parameter estimates. Thequarter dummies, for each four-digit SIC industry, controlfor seasonality. The regression variables are scaled by thefirm'sbeginning-of-quartertotal assets to correctfor hetero-skedasticity. We estimate equation (2) augmented by thefinancial variables: cash stocks, coverage, and cash flow.A concern in the literature s that financial variables couldbe correiated with expectations of future fundamentals notcaptured by "control variables." This potential problem isless severe with inventory investmentregressions. Inventoryinvestment is a high-frequency phenomenon compared, forexample, to fixed capital investment. The horizon forexpectations is short, and the relevant expectations shouldbe adequately proxied by current sales. Nevertheless, weinclude two quarterlyleads of sales in some regressions toprovide furtherassurance that our results are not driven byexpectations.6B. Constructionof the Panels

Our data are taken from the Compustat quarterly tapes, asource virtually untapped in earlier research. To protectagainst results driven by extreme observations, we excludedatain the 1%tails of the distributionfor each variable.7Wedivided the data into three panels along the time dimensionat peaks in aggregate inventory investment so that eachperiod contains a distinct inventory cycle. Period 1 runsfrom 1981.3 to 1984.1, period 2 from 1984.2 to 1988.3, and

FIGURE 1 -INVENTORY GROWTH RATES0.04-

Durables

0.02AA

6070 75 80 85 90Source: Citibase. Each series is a three-quartermoving averageof quarterly nventorygrowth rates.

6 GG indicate hat hemainconcern orinventorymodels s correlationbetween expected sales and financialvariables.Gilchristand Zakrajsek(1995) point out that structuralmodels of inventoriesemploy salesexpectations ather hanprofit xpectations.Chirinko ndSchaller 1995)consider whether evidence of financingconstraints s due to omittedexpectations nd he endogeneity f financial ariables.7 Outliers were identified after subtracting irm and quartermeans.Resultswere similarwhen he cutoff or outlierswas changed o either0.5or 2%.


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COMPARATIVE TUDY OF FINANCING CONSTRAINTSAND INVENTORYINVESTMENT 515

period 3 from 1988.4 to 1992.4. Each panel is balanced. Wedefined the durable and nondurable sectors with the usualtwo-digit SIC categories. The final sample contains approxi-mately one-half of aggregate sales and inventories inmanufacturing.Further nformation about the data construc-tion is given in the data appendix.A common approachin the financing constraint literatureis to separate firms into groups according to a priori criteriathat relate to the presence of financing constraints.Research-ers have used a variety of criteria to categorize firms (seeHubbard (1998)). We split the sample by firm size, as iscommon in the literature (e.g., GG, CFP, and Chevalier andScharfstein (1995)). Small firms face higher transactioncosts for external finance, rarely have bond or commercialpaper ratings, andmay have greater nformationproblems incapitalmarkets. We define small firmsas those with less than$300 million in average total assets (1987 dollars), similar tocutoffs used elsewhere.8C. Characteristicsof thePanels

Table 1reports sample summary statistics. Large firms arean order of magnitude larger than small firms. The medianretentionratio (the ratio of income less dividends to income)is always largerfor small firms thanfor large ones. In all butone case the median small firmretainsmore than 80% of itsincome. High retention ratios for small firms are consistentwith the view that small firms aremore likely to face bindingfinancing constraints.Furthermore, nternal finance is over-whelmingly the largest source of funds. The median shareofcash flow in total sources of finance exceeds 80% in all butone instance, andis more than 90% in the majorityof cases.9

D. Movementsof Inventoriesand Financial VariablesacrossSectors and TimeFigure 2 displays seasonally adjusted means for inventoryinvestment and the three financial variables. (Heavy linesrepresent durables and lighter lines nondurables.) The vari-

ables (except for coverage) are scaled by the firms' totalassets. We define coverage as interestexpense divided by thesum of interest expense plus cash flow. The three inventoryinvestment cycles are evident in figure 2. Consistent with theaggregate evidence, inventoryinvestment for both small andlarge firms is considerably more cyclical for durables thanfor nondurables.Like inventories, all three financial variables displaysubstantial time-series variation. The patternof cash flow,however, most closely resembles the cyclical and sectoralpatterns of inventory investment. The cash flow meansdisplay three distinct cycles, corresponding closely to thetiming of the inventory investment cycles. The firsttwo cashflow cycles are more pronounced for durables than fornondurables, which may help explain the greater cyclicalvolatility of durable sector inventory investment in theseperiods. This patternof cash flow is consistent with aggre-gate data: the percentagedecline in durablebusiness incomeduring the last six recessions averaged 60%, whereas thecorrespondingdecline for nondurables averaged only 13%.

III. Regression ResultsTable2 presents estimates of equation (2) augmentedwiththe three financial variables. Standard errors corrected for

heteroskedasticity appear in parentheses. Results for thestock adjustment parametersare consistent with those in theinventory literature(e.g., Blinder (1986)). The coefficientson the lagged inventory stock variable are negative andhighly significant. The sales variables almost always havepositive coefficients.Turningto the first of the financial variables, we includebeginning-of-quarter cash stocks, as in KLS. In the firstperiod, the cash stock coefficient for small nondurable firmsis statistically significant and the coefficient is insignificantfor large firms. While this pattern s consistent with the KLSfindings for a similar time period, the coefficient for smallnondurable firms (0.102) is only about 30% as large as theircross-sectional estimate. For durable firms, the cash stockcoefficients aresmall and statistically insignificant and differlittle across firm size. There are no statistically significantcash stock coefficients in period 2. KLS also find nosignificant effect of cash stocks in the middle 1980s andargue that this finding is consistent with the bank lendingchannel, given the loose stance of monetary policy duringthatperiod.IOIn the thirdperiod, however, bank lending wasarguably restricted due to monetary policy and the "credit

TABLE 1.-SAMPLE MEDIANSPeriod 1 Period2 Period 3(81.3-84.1) (84.2-88.3) (88.4-92.4)

Small Large Small Large Small LargeFirms Firms Firms Firms Firms FirmsNumberof firmsNondurables 87 107 139 115 166 139Durables 129 116 284 110 394 138TotalassetsNondurables 115.3 1724.2 82.3 1470.7 73.5 1367.6Durables 99.6 1217.4 60.5 1138.4 58.7 1162.2InventoriesNondurables 23.0 310.9 15.9 215.8 15.1 204.2Durables 27.2 257.8 15.4 207.5 14.0 199.5RetentionratioNondurables 0.758 0.574 0.827 0.636 0.892 0.609Durables 0.836 0.670 1.000 0.737 1.000 0.802Cash flow tonet sourcesNondurables 0.898 0.934 0.932 0.900 0.962 0.900Durables 0.774 0.935 0.859 0.833 0.915 0.812Source:Authorscomputations rom Compustatdata.Totalassets, inventoriesand sales expressedas

millionsof 1987 dollars.

8 See Scherer ndRoss(1990, table3.1) andGG.9Totalsourcesequalsthe sumof cashflow,the value of equity ssues,and he change n debt. 10We point out that becausethe bank lendingchannelrequires ightmonetary olicy, t cannot xplain he nventoryycle in the secondperiod.


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516 THE REVIEW OF ECONOMICSAND STATISTICS

crunch."'" There is no evidence of positive cash stockeffects for small firmsin either the durable or the nondurablesector in this period. Thus when we extend the KLSempirical test to the third period, the results provide nosupport for the bank lending channel during this inventorycycle. 12One explanation for the relatively weak performance ofcash stocks is that this variablemay proxy for access to debt,but access to debt may not vary much in the time dimensionof the data. Nevertheless, cash stocks may be importantin

explaining cross-sectional inventory investment (as in KLS).We do not examine between-firm variation, however, be-cause financial variables are likely to be correlated withunobservable firm effects, a shortcoming of cross-sectionalregressions.The second financial variable is coverage lagged onequarter, as in GG. The coverage ratio has the expectednegative sign in all twelve regressions, and is statisticallysignificant in five cases. The evidence for firm-sizeheteroge-neity is mixed. There is strong evidence of largereffects forsmall firms in two of the six comparisons possible (period 2nondurables and period 3 durables). There is no clear patternfor the coverage coefficients across either sectors or time.Including contemporaneous coverage or an additional lag ofcoverage had little effect on the results.One explanationfor weaknesses in the coverage results ismeasurementerror."3 ecause coverage is a ratio, it may be

FIGURE 2.-MEAN VALUES OF INVENTORYINVESTMENTAND FINANCIAL VARIABLESInventory Change - Small Firms Cash Flow - Small Firms Cash / Equivalents - Small Firms Coverage - Small Firms

0.02 0.036 0.16 0.4

0.015 0.14 0.350.03 0.3

0.01 0.120.25

0.00 0 0.024 0.10.2

0 0.08 0.100.018-0.000 0.06 0.1

InvrenAthoryhangculargen iroms CashstaFlow.l vralesarge FirmseCyf ttlashets Hequvalents eLargsnapeims aso Coverageims larghlies irm

0.02u e0.03 0.16 0.4-0.010I I I I I I 0.1 0.30.. . I . . .

0.03( O - .0 0

0.2 103 0.12 .

0.20

0.000 0.024 0.10.2

0 0.080.018 01

-0.005 0.06 - .

Source: Authors calculationsfrom Compustatdata. All variablesare divided by firm total assets. Heavylines represent sample means for durable irms, light lines fornondurablefirms.

11Changes in banking regulation and large declines in the value of bankcapital may have reduced the supply of bank loans. See the review,including empirical evidence, in Boyd and Gertler (1994, pp. 21-22) andPeek and Rosengren (1995). Bemanke (1993) argues that the typicalsymptoms of tight money were not present during the 1990-1991recession. He also notes, however, that the decline in loan growth duringthis period was worse than for a typical recession, possibly due to shocks tobank capital.12 KLS split the sample according to the presence of a bond rating. Wealso split by bond ratings and found cash stock results similar to those intable 2. This is not surprising because there is a high correlation betweenfirm size andthe presence of a bond rating.

13 The cash stock variable is less likely to be measured with errorbecauseit is an audited balance-sheet variable.


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more sensitive to measurement errorin micro data than inaggregate data, such as those used by GG. We attempt toaddress this problem by defining coverage in robust ways(excluding firms with negative cash flows and placinginterest expense in both the numeratorand the denominator).The third financial variable is cash flow. We includecontemporaneous and two lags of cash flow, as in CFP. Forexpediency, we discuss only the sums of the cash flowcoefficients. The sums are positive in every case except forlarge firms in period 3. For both sectors in all three periodssmall firms have bigger cash flow sums than large firms. Thedifferences are usually quite pronounced. Combining thethree periods, a Wald test (with a consistent covariancematrix) rejects the equality of cash flow sums across firmsize with p-values of 0.01 for nondurables and 0.03 fordurables. Cash flow is significant, statistically and economi-cally, for small firms in five of six regressions. Even thoughinventories account for only a small fraction of firminvestment on average, these reduced-form regressionssuggest that 15% to 40% of cash flow fluctuations are

absorbed by inventory investment for small firms.'4Acrosssectors, the cash flow sums are at least as large for durablesas for nondurables in each period. This result, combinedwith the greateramplitudeof internal finance fluctuations fordurable industries, helps explain the sectoral difference ininventory volatility.We performed several additional tests to check therobustness of the results. To explore the possibility thatcollinearity among financial variables may mask their indi-vidual effects, we estimated equation (2) with each financialvariable alone. The patterns and magnitude of coefficientsare similar to those in table 2 for all threefinancial variables.As mentioned, we included two quarterly leads of sales.Sales leads should greatly reduce the impact of the financialvariables if these variables simply proxied for expectations.The cash flow sums, however, and the differences betweensmall and large firms were remarkably stable when leads of

TABLE 2.-INVENTORY INVESTMENT REGRESSIONS WITH FINANCIAL VARIABLESNondurables Durables

Small Firms Large Firms Small Firms Large FirmsPeriod 1 81.3-84.1

Nj,t -0.288 (0.050) -0.395 (0.044) -0.253 (0.041) -0.267 (0.055)Sj,t -0.069 (0.029) 0.027 (0.039) -0.024 (0.025) 0.085 (0.031)Sj,t-l 0.076 (0.024) -0.003 (0.025) 0.097 (0.032) -0.004 (0.030)Si t-2 0.074 (0.024) 0.093 (0.024) -0.003 (0.030) 0.034 (0.025)Cash Stocksj,t 0.102 (0.027) 0.005 (0.023) -0.001 (0.026) 0.011 (0.027)Coveragej,t-1 -0.0053 (0.0068) -0.0085 (0.0050) -0.0077 (0.0026) -0.0054 (0.0022)CFj,t 0.038 (0.118) 0.058 (0.052) 0.254 (0.082) -0.004 (0.063)CFj,t- 1 0.141 (0.108) 0.169 (0.075) 0.181 (0.114) 0.277 (0.093)CFj,t-2 0.073 (0.129) -0.112 (0.059) -0.018 (0.089) -0.087 (0.070)Cash Flow Sum 0.252 (0.180) 0.115 (0.092) 0.417 (0.134) 0.186 (0.102)Adj.R2 0.173 0.206 0.127 0.119Period 2 84.2-88.3Nj,t --0.318 (0.033) -0.304 (0.048) -0.168 (0.017) -0.112 (0.029)Sj,t -0.049 (0.023) 0.078 (0.039) -0.004 (0.017) 0.051 (0.033)Sj,t-1 0.055 (0.023) 0.045 (0.025) 0.053 (0.016) 0.052 (0.021)SJ,t-2 0.057 (0.020) -0.003 (0.019) 0.014 (0.014) -0.024 (0.020)Cash Stocksj,t 0.015 (0.019) 0.010 (0.015) 0.002 (0.011) -0.017 (0.013)Coverage,t-1 -0.0171 (0.0046) -0.0008 (0.0044) -0.0030 (0.0018) -0.0032 (0.0029)CFj 0.222 (0.067) -0.146 (0.070) 0.160 (0.033) 0.045 (0.046)CFj,t- 1 --0.063 (0.075) 0.128 (0.074) 0.121 (0.046) 0.081 (0.054)CFJ,t-2 0.086 (0.071) 0.074 (0.040) 0.087 (0.031) 0.089 (0.032)Cash Flow Sum 0.245 (0.108) 0.056 (0.075) 0.368 (0.060) 0.216 (0.071)Adj.R2 0.130 0.124 0.082 0.043Period 3 88.4-92.4Nj,t --0.327 (0.031) -0.411 (0.036) -0.185 (0.017) -0.182 (0.025)Si't -0.114 (0.023) 0.047 (0.022) -0.019 (0.017) 0.035 (0.026)Sj,t-1 0.062 (0.022) 0.086 (0.026) 0.067 (0.017) 0.086 (0.023)Si t-2 0.082 (0.023) 0.013 (0.024) 0.035 (0.015) 0.018 (0.018)CashStocksj,t -0.035 (0.015) -0.010 (0.016) 0.012 (0.010) 0.049 (0.014)Coveragej,t- -0.0004 (0.0031) -0.0041 (0.0017) -0.0049 (0.0013) -0.0018 (0.0011)CFj,t 0.096 (0.014) 0.066 (0.033) 0.160 (0.034) -0.001 (0.031)CFj,t- 1 --0.009 (0.064) -0.099 (0.057) 0.001 (0.037) -0.005 (0.048)CFJ,t-2 0.067 (0.046) -0.020 (0.030) 0.024 (0.027) -0.015 (0.025)Cash Flow Sum 0.154 (0.077) -0.053 (0.056) 0.185 (0.051) -0.021 (0.056)Adj.R2 0.153 0.153 0.079 0.104

Source:AuthorscomputationsromCompustatdata.Fixedfirmand ime effectsnotreported.White-adjustedtandard rrors n parentheses.The R2statisticsdo notincludethevarianceexplained by either hefirmdummiesor seasonal time dummies.

14 Cash flow effects may be large becauseinventoriesare liquid andreversible ssets(see CFP).


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518 THE REVIEWOF ECONOMICSAND STATISTICSsales were addedto the regressions.The cash stock andcoverageresultswere also largelyunaffectedby includingleads of sales. Finally,we consideredthe effect of costshocks.Weincludedreal wages,real energycosts,andrealinterestrates,both as contemporaneousnd lagged levelsandas first differences.'5The coefficients or the financialvariablesdecline somewhatfor first-perioddurables,dueprimarily o a significanteffect of energy costs, but thepatternof resultsacross firmsize andsector s unchanged.The financial ariable oefficientsarevirtuallyunaffectednthesecondandthirdperiods.

IV. ConclusionThispapercompares he empiricalperformance f threevariablesusedto test the impactof financing onstraints ninventorynvestment.Ourhigh-frequencyquarterly)anelof microeconomicdatais a substantialmprovement verthe data used in most previoustests. In additionto the

well-knowneconometric dvantages f panel data, he dataaidcomparison y allowingus to examinedifferencesn theresultsacross finn size, two industrial ectors,and threedistinct nventory ycles. All three financialvariableshavesomesignificantffectsoninventorynvestment.Acompari-son of the emipirical erformance f these variables,how-ever, shows that cash flow is the most successful inexplaining hefacts about nventorynvestmentacrossfirmsize and acrossdifferent imeperiods.Cashflowalso helpsexplainthe greatercyclical volatilityof inventory nvest-ment nthedurables ector.These results have potentialpolicy implications.Theinventoryinvestment regressionsin GG and KLS havereceived a greatdeal of attention.'6They have been inter-pretedas support, espectively,or a balancesheet andbanklendingchannel n the monetary olicytransmissionmecha-nismi.Our indings uggest hat heseresultsaresensitive othe structureof the data used in empiricaltests. It isprematureo drawstrongpolicyconclusions romtheeffectof cash stocksorcoverageon inventorynvestment.But theempiricalsuccess of cash flow in tests based on high-frequencypaneldata s consistentwith a significant ole forfinancing onstraintsn the transmissionmechanism:mon-etaryshocks can inducechanges n demandandcash flowthat contributeo the "financialaccelerator" iscussedbyBernanke t al. (1996).

Ourresults uggest hatvariationn cashflowis importantfor understandinghe role financing onstraints lay in thebusiness cycle. Internal inanceis the dominant orm offinance or most firmsand ts largecyclicalvariations oneof theprincipal tylizedbusinesscycle facts notedby Lucas(1977).A largefractionof the modem corporation'sosts

are fixed or quasi-fixed in the short run. Relatively smalldemand shocks, therefore,can induce relatively large move-ments in cash flow, which in turn can generate cyclicalinventory fluctuations for financially constrainedfirms thatmagnify the impact of the shock. The potential for cash flowto have this kind of direct effect in propagating real andmonetary shocks to the economy has been underemphasizedand deserves more attention in futureresearch.

15 See Macciniand Rossana 1984). The wage data from the MonthlyLabor Review and the energy data from the Monthly Energy Review aredefinedat the two-digitSIC evel.The real nterest ate s thethree-monthTreasuryillrate ess inflationmeasured y the GDPdeflator.16 See, for example, he Journal of Economic Perspectives (Fall 1995,vol. 9, no. 4, 3-96) and heNBERReporter Fall1995, 6-9.)

REFERENCESBernanke,Ben, "Credit nd he MacroEconomy,"FederalReserveBankof New YorkQuarterlyReview 18 (1993), 50-70.Bernanke,Ben S., and MarkGertler,"Inside he Black Box: The CreditChannelof MonetaryPolicy Transmission,"ournalof EconomicPerspectives 9 (1995), 27-48.Bernanke,Ben S., Mark Gertler,and Simon Gilchrist,"The FinancialAcceleratornd heFlight oQuality,"hisREVIEW 8 (1996),1-15.Blinder, Alan S., "More on the Speed of Adjustment n InventoryModels," Journal of Money, Credit, and Banking 18 (1986),355-365.Blinder,Alan S., and Louis J. Maccini, "TakingStock: A CriticalAssessmentof RecentResearchon Inventories," ournalof Eco-nomic Perspectives 5 (1991), 73-96.Boyd,JohnH., and MarkGertler,"AreBanksDead?OrAre theReportsGreatly Exaggerated?," Federal Reserve Bank of MinneapolisQuarterlyReview (Summer 1994), 2-23.Calomiris, CharlesW., Charles P. Himmelberg,and Paul Wachtel,"CommercialPaperand CorporateFinance:A MicroeconomicPerspective," Carnegie-Rochester Conference on Public Policy 42(1995), 203-250.Carpenter, obertE., StevenM. Fazzari,andBruceC. Petersen,"Inven-tory Investment, nternal-Financeluctuations, nd the BusinessCycle," Brookings Papers on Economic Activity 2 (1994), 75-138.Carpenter,Robert E., and Daniel Levy, "Seasonal Cycles, BusinessCycles,and heComovementf InventorynvestmentndOutput,"Journal of Money, Credit,and Banking 30 (1998), 331-346.Chevalier,udithA., andDavidS. Scharfstein, LiquidityConstraintsnd

the Cyclical Behavior of Markups,"American Economic Review 85(1995),390-396.Chirinko,RobertS., andHuntleySchaller,"WhyDoes LiquidityMatternInvestmentEquations?",Journal of Money, Credit,and Banking 27(1995),527-548.Gertler,Mark,and SimonGilchrist,"Monetary olicy,BusinessCyclesand the Behavior of Small Manufacturing irms," QuarterlyJournal of Economics 109 (1994), 309-340.Gilchrist,Simon, and Egon Zakrajsek,"TheImportance f CreditforMacroeconomic ctivity: dentificationhroughHeterogeneity,"nIs Bank Lending Important for the Transmission of MonetaryPolicy?, FederalReserve Bank of Boston ConferenceSeries 39(1995), 129-158.Hubbard,R. Glenn, "Capital-Marketmperfectionsand Investment,"Journal of Economic Literature 35 (1998), 193-225.Kashyap,Anil K., Owen A. Lamont,and JeremyC. Stein, "CreditConditionsand the CyclicalBehaviorof Inventories,"QuarterlyJournal of Economics 109 (1994), 565-592.Lovell,MichaelC., "Researchingnventories:WhyHaven'tWe LearnedMore?,"International Journal of Production Economics 35 (1994),33-41.Lucas,Robert,E., "Understanding usinessCycles," n Stabilization fthe Domestic and International Economy, Carnegie-RochesterSeries nPublicPolicy24 (1977),7-29.Maccini,LouisJ.,andRobert .Rossana,"JointProduction,Quasi-FixedFactorsof Production,ndInvestmentn FinishedGoodsInvento-ries," Journal of Money, Credit, and Banking 16 (1984), 218-236.Milne, Alastair,"FinancialEffects on Inventory nvestment,"mimeo,University f Surrey,EnglandNov., 1995).Peek,Joe,andEricS. Rosengren, CrunchingheRecovery:BankCapitaland the Role of BankCredit,"n LynneE. Browne andEric S.Rosengren (eds.), Real Estate and the Credit Crunch (Boston:FederalReserveBankof Boston,1995),151-186.


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DATAAPPENDIXCompustat ontains balance sheet andincomestatementdatacompiled in afiscal-year ormat.We use the company'sreported iscalyear end to align fiscalquarterswith calendarquarters.We adjust nventoriesto account for the biasintroduced y historical ost accounting.Wegroup irms nto LIFO (last-in-first-out) and non-LIFO categories. For LIFO firms, we apply an algorithmdeveloped by Salinger and Summers(1983) to estimate the replacementvaluefor the inventory stock. For FIFO (first-in-first-out)firms, the change ininventorieswill be overstated f there is a positive inflation rate because theend-of-periodvalue will include the nominal nflationof the stocks. To remove

the inflationbias from FIFO firms' nventory nvestmentvariable,we computethe change of inventories after deflating the stocks. For LIFO firms, weconstruct he flow measure of inventory nvestmentby differencing he stock,then deflating.We define cash flow as income before extraordinarytems plus the sum ofnon-cash charges against income. The stock of cash is defined as cash andshort-term nvestments. Coverage is interest expense divided by the sum ofinterestexpense plus cash flow. (GG define coverage as cash flow divided byshort-termnterestexpense. This definitioncausesproblems n micro datasincea numberof firmsin our sample have no debt.) GG proxy short-term nterestexpense by multiplying he stock of short-termdebtby the primerate. We usethe sameapproachwiththethree-month ommercialpaperrate.We delete firmsthat have negative coverage ratios due to negative cash flows. To construct areal measure for sales, we divide sales by the implicitGNPprice deflator.Weuse the implicit price deflator for nonresidential nvestment to constructallotherreal variables.We defined the durableand nondurable irms with the usualtwo-digitSICcategories.Nondurablemanufacturing onsists of SIC codes 20-23 and 26-3 1.Durable firms are in SIC codes 24, 25, and 32-38. We deleted miscellaneousmanufacturingirms (SIC 39) fromthe sampleas well as firmsthat were notincorporated n the United States. We also deleted firms with less than $10million in total assets, which account for only a small fraction of inventoryinvestment.These firms arefrequently start-up ompanieswith zero sales andnegative cash flow. Even thougha firm is excluded from one period, it mayenter others f it reaches$10 million in assets.

Salinger,MichaelA., and LawrenceH. Summers, "Tax Reform andCorporate nvestment:A MicroeconomicSimulationStudy," nMartin Feldstein (ed.), Behavioral Simulation Methods in TaxPolicy Analysis (Chicago:University of ChicagoPress, 1983),247-286.Scherer, F.M., and David Ross, Industrial MarketStructure and EconomicPerformance, rded. (Boston:Houghton-Mifflin,990).Stanback, Thomas M., Jr.,Postwar Cycles in Manufacturers'Inventories,NationalBureau f EconomicResearch1962).Zarnowitz,Victor, "Recent Work on Business Cycles in HistoricalPerspective:A Review of TheoriesandPerspectives," ournalofEconomic Literature23 (1985), 523-580.

fixed effect 2 way error

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